scholarly journals Analysis of Particulate Matter Concentration Intercepted by Trees of a Latin-American Megacity

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 723
Author(s):  
Carlos Zafra-Mejía ◽  
Joaquín Suárez-López ◽  
Hugo Rondón-Quintana
Keyword(s):  
Particulate Matter ◽  
Ecosystem Services ◽  
Latin American ◽  
Urban Areas ◽  
Ligustrum Lucidum ◽  
Particulate Matter Concentration ◽  
Air Pollutions ◽  
The Relationship ◽  
Matter Concentration ◽  
Pm2.5 Concentration

Urban areas with trees provide several ecosystem services to citizens. There is a growing interest in ecosystem services for the removal of air pollutants such as particulate matter. The objective of this paper is to show a study on the variation of intercepted particulate matter concentration (IPMC) by tree leaves in the megacity of Bogotá (Colombia). The relationship between IPMC and PM2.5 concentrations observed in air quality stations in two urban zones with different air pollutions was studied. Influences of climate and leaf morphology variables on IPMC were also analyzed. The species under study were Ligustrum-lucidum, Eucalyptus-ficifolia, Tecoma-stans, Callistemon-citrinus, Lafoensia-acuminata, and Quercus-humboldtii. The results showed that leaf IPMC decreased as the PM2.5 concentration increased. Species that best described this trend were Ligustrum-lucidum and Lafoensia-acuminata. These two species also showed the largest IPMC in their leaves. Indeed, species that showed the largest leaf area were those with the highest IPMC. On average, it was observed that for each 5.0 µg/m3 increase in PM2.5 concentration the IPMCs of the species Ligustrum-lucidum and Lafoensia-acuminata decreased by 33.6% and 23.1%, respectively. When wind speed increased, there was also an increase in PM2.5 concentrations and a reduction in the leaf IPMCs.

scholarly journals Use of low-cost PM-sensors to determine the infiltration of outdoor particles into indoor environments

2019 ◽  
Vol 111 ◽  
pp. 02026
Author(s):  
Jan Drzymalla ◽  
Andreas Henne
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Indoor Environments ◽  
Research Project ◽  
Private Households ◽  
Particulate Matter Concentration ◽  
High Concentrations ◽  
Pm2.5 And Pm10 ◽  
The Relationship ◽  
Matter Concentration

Whether due to traffic, industry or private households – particulate matter enters our air every day and pollutes the air we breathe. When the term air pollution is used, hardly anyone ever thinks of the air inside their own home. However, many urban residences are located in the immediate vicinity of busy roads with high concentrations of particulate matter. Consequently, the outside concentration of fine dust has considerable influence on the indoor concentration. Given the fact that many people spend more than 90 % of their lifetime indoors, it is important to measure and understand particle transport from the outside to the inside in order to assess the effects of exposure to outdoor particles on human health. A two-room apartment near a main road in Leverkusen, North Rhine-Westphalia, Germany was used in the investigation in this research project. Particulate matter concentrations for PM2.5 and PM10 were measured simultaneously inside and outside of the building. Results are size-specific deposition rates, indoor/outdoor ratios and infiltration factors, which provide information on the relationship between internal and external concentrations and the associated health consequences. The particulate matter concentration was measured using low-cost PM-sensors, which were developed and calibrated within the scope of this research project.

scholarly journals TRAINING RECURRENT NEURAL NETWORKS FOR PARTICULATE MATTER CONCENTRATION PREDICTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1575-1582
Author(s):  
C. J. Masinde ◽  
J. Gitahi ◽  
M. Hahn
Keyword(s):  
Neural Networks ◽  
Particulate Matter ◽  
Recurrent Neural Networks ◽  
Urban Areas ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Particulate Matter Concentration ◽  
Matter Concentration ◽  
Gated Recurrent Unit

Abstract. A high level of particulate matter in the atmosphere has an adverse long-term effect on human health. It has been associated with increased pulmonary tract and lung infections. It is more common in urban areas, especially megacities due to the confluence of industries and motorized machinery. Considering that most of the world’s population lives in urban areas, there is a need to monitor air pollution arising from particulate matter in order to ensure clean and safe air in cities in accordance with goal 11 of the Sustainable Development Goals. One way of doing this is through the use of Recurrent Neural Networks (RNN), which are suited for time varying data. Particulate matter concentration recorded by a network of low-cost sensors in Stuttgart is trained on three of the most popular RNN variants: Standard LSTM, Peephole LSTM and Gated Recurrent Unit. Two optimizers are used, Stochastic Gradient descent and Adam. Training is done on a single sensor and the optimum weights transferred and used in the prediction of other sensor values. This study concludes that Gated Recurrent Unit with Stochastic Gradient Descent is the most effective of the three variants in predicting particulate matter PM2.5 concentrations. In addition to this, weight transfer between sensors is not affected by temperature, wind direction, wind speed and geographic distance between sensors but rather by atmospheric pressure and the similarity of recorded Particulate matter levels.

scholarly journals An Urban Lagrangian Stochastic Dispersion Model for Simulating Traffic Particulate-Matter Concentration Fields

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 580
Author(s):  
Eyal Fattal ◽  
Hadas David-Saroussi ◽  
Ziv Klausner ◽  
Omri Buchman
Keyword(s):  
Surface Layer ◽  
Particulate Matter ◽  
Urban Area ◽  
Hybrid Approach ◽  
Urban Morphology ◽  
Dispersion Pattern ◽  
Vertical Column ◽  
Particulate Matter Concentration ◽  
Vertical Heterogeneity ◽  
Matter Concentration

The accumulated particulate matter concentration at a given vertical column due to traffic sources in urban area has many important consequences. This task, however, imposes a major challenge, since the problem of realistic pollutant dispersion in an urban environment is a very demanding task, both theoretically and computationally. This is mainly due to the highly inhomogeneous three dimensional turbulent flow regime in the urban canopy roughness sublayer, which is far from “local equilibrium” between shear production and dissipation. We present here a mass-consistent urban Lagrangian stochastic model for pollutants dispersion, where the flow field is modeled using a hybrid approach by which we model the surface layer based on the typical turbulent scales, both of the canopy and in the surface layer inertial sub-layer. In particular it relies on representing the canopy aerodynamically as a porous medium by spatial averaging the equations of motion, with the assumption that the canopy is laterally uniform on a scale much larger than the buildings but smaller than the urban block/neighbourhood, i.e., at the sub-urban-block scale. Choosing the spatial representative averaging volume allows the averaged variables to reflect the characteristic vertical heterogeneity of the canopy but to smooth out smaller scale spatial fluctuations caused as air flows in between the buildings. This modeling approach serves as the base for a realistic and efficient methodology for the calculation of the accumulated concentration from multiple traffic sources for any vertical column in the urban area. The existence of multiple traffic sources impose further difficulty since the computational effort required is very demanding for practical uses. Therefore, footprint analysis screening was introduced to identify the relevant part of the urban area which contributes to the chosen column. All the traffic sources in this footprint area where merged into several areal sources, further used for the evaluation of the concentration profile. This methodology was implemented for four cases in the Tel Aviv metropolitan area based on several selected summer climatological scenarios. We present different typical behaviors, demonstrating combination of source structure, urban morphology, flow characteristics, and the resultant dispersion pattern in each case.

scholarly journals Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity, India

2021 ◽  
Vol 67 (7) ◽  
pp. 2140-2150
Author(s):  
V. Sreekanth ◽  
Meenakshi Kushwaha ◽  
Padmavati Kulkarni ◽  
Adithi R. Upadhya ◽  
B. Spandana ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Particulate Matter Concentration ◽  
Fine Particulate ◽  
Matter Concentration ◽  
Concentration Levels
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